Data from several sources were joined together into a merged dataset. We used 2017 year to build the model. Main outcome is suicide rate for each state, candidate predictors are gun, alchohol, temperature, precipitation, marijuana, education, gdp and gender for each state. We used stepwise approach to select model.
| Predictor | Description |
|---|---|
| suicide | Suicide rate per 100000 population |
| gun | Number of guns per 1000 population |
| alcohol | Alcohol consumption per capita (gallons of ethanol) |
| temperature | Average temperature (F) |
| precipitation | Average precipitation (inches) |
| marijuana | Marijuana use in adults (%) |
| education | Educational attainment - bachelor’s degree or higher (%) |
| gdp | GDP per capita (dollars) |
| gender | Male (%) |
| term | estimate | p.value |
|---|---|---|
| (Intercept) | -143.9870 | 0.0000 |
| gun | 0.1220 | 0.0007 |
| temperature | -0.0955 | 0.0502 |
| marijuana | 0.2485 | 0.0038 |
| education | -0.2343 | 0.0085 |
| gender | 3.5589 | 0.0000 |
| gdp | -0.0002 | 0.0004 |
| r.squared | adj.r.squared | AIC | BIC |
|---|---|---|---|
| 0.8354 | 0.8124 | 220.9604 | 236.2566 |
The fitted equation is “suicide = -143.99 + 0.12gun - 0.10temperature + 0.25marijuana - 0.23education + 3.56gender - 0.0002gdp”.
As the regression shows, “gun”, “marijuana”,“education”,“gender”,“gdp” are significant predictors for suicide rate. Adjusted R-square is 0.8124, which means these variables can explain a large proportion of variance in the suicide rate. According to the results, suicide rate is larger in states where have a higher gun ownership rate, higher marijuana usage, higher ratio of males to females, lower temperature and lower educational attainment.